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Pure intelligent monitoring system for steam economizer trips

Steam economizer represents one of the main equipment in the power plant. Some steam economizer's behavior lead to failure and shutdown in the entire power plant. This will lead to increase in operating and maintenance cost. By detecting the cause in the early stages maintain normal and safe op...

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Main Authors:	Basim Ismail, F., Hamzah Abed, K., Singh, D., Shakir Nasif, M.
Format:	Article
Published:	EDP Sciences 2017
Online Access:	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85033229462&doi=10.1051%2fmatecconf%2f201713104008&partnerID=40&md5=70ddacc6d016e7a6029f50bd303584e8 http://eprints.utp.edu.my/19962/
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id	my.utp.eprints.19962
record_format	eprints
spelling	my.utp.eprints.199622018-04-22T14:27:56Z Pure intelligent monitoring system for steam economizer trips Basim Ismail, F. Hamzah Abed, K. Singh, D. Shakir Nasif, M. Steam economizer represents one of the main equipment in the power plant. Some steam economizer's behavior lead to failure and shutdown in the entire power plant. This will lead to increase in operating and maintenance cost. By detecting the cause in the early stages maintain normal and safe operational conditions of power plant. However, these methodologies are hard to be achieved due to certain boundaries such as system learning ability and the weakness of the system beyond its domain of expertise. The best solution for these problems, an intelligent modeling system specialized in steam economizer trips have been proposed and coded within MATLAB environment to be as a potential solution to insure a fault detection and diagnosis system (FDD). An integrated plant data preparation framework for 10 trips was studied as framework variables. The most influential operational variables have been trained and validated by adopting Artificial Neural Network (ANN). The Extreme Learning Machine (ELM) neural network methodology has been proposed as a major computational intelligent tool in the system. It is shown that ANN can be implemented for monitoring any process faults in thermal power plants. Better speed of learning algorithms by using the Extreme Learning Machine has been approved as well. © The authors, published by EDP Sciences, 2017. EDP Sciences 2017 Article PeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85033229462&doi=10.1051%2fmatecconf%2f201713104008&partnerID=40&md5=70ddacc6d016e7a6029f50bd303584e8 Basim Ismail, F. and Hamzah Abed, K. and Singh, D. and Shakir Nasif, M. (2017) Pure intelligent monitoring system for steam economizer trips. MATEC Web of Conferences, 131 . http://eprints.utp.edu.my/19962/
institution	Universiti Teknologi Petronas
building	UTP Resource Centre
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Petronas
content_source	UTP Institutional Repository
url_provider	http://eprints.utp.edu.my/
description	Steam economizer represents one of the main equipment in the power plant. Some steam economizer's behavior lead to failure and shutdown in the entire power plant. This will lead to increase in operating and maintenance cost. By detecting the cause in the early stages maintain normal and safe operational conditions of power plant. However, these methodologies are hard to be achieved due to certain boundaries such as system learning ability and the weakness of the system beyond its domain of expertise. The best solution for these problems, an intelligent modeling system specialized in steam economizer trips have been proposed and coded within MATLAB environment to be as a potential solution to insure a fault detection and diagnosis system (FDD). An integrated plant data preparation framework for 10 trips was studied as framework variables. The most influential operational variables have been trained and validated by adopting Artificial Neural Network (ANN). The Extreme Learning Machine (ELM) neural network methodology has been proposed as a major computational intelligent tool in the system. It is shown that ANN can be implemented for monitoring any process faults in thermal power plants. Better speed of learning algorithms by using the Extreme Learning Machine has been approved as well. © The authors, published by EDP Sciences, 2017.
format	Article
author	Basim Ismail, F. Hamzah Abed, K. Singh, D. Shakir Nasif, M.
spellingShingle	Basim Ismail, F. Hamzah Abed, K. Singh, D. Shakir Nasif, M. Pure intelligent monitoring system for steam economizer trips
author_facet	Basim Ismail, F. Hamzah Abed, K. Singh, D. Shakir Nasif, M.
author_sort	Basim Ismail, F.
title	Pure intelligent monitoring system for steam economizer trips
title_short	Pure intelligent monitoring system for steam economizer trips
title_full	Pure intelligent monitoring system for steam economizer trips
title_fullStr	Pure intelligent monitoring system for steam economizer trips
title_full_unstemmed	Pure intelligent monitoring system for steam economizer trips
title_sort	pure intelligent monitoring system for steam economizer trips
publisher	EDP Sciences
publishDate	2017
url	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85033229462&doi=10.1051%2fmatecconf%2f201713104008&partnerID=40&md5=70ddacc6d016e7a6029f50bd303584e8 http://eprints.utp.edu.my/19962/
_version_	1738656144090464256
score	13.211869

Pure intelligent monitoring system for steam economizer trips

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